scholarly journals A signature predictive of early versus late recurrence after radiation (RT) for breast cancer that may inform the biology of early, aggressive recurrences.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 112-112
Author(s):  
Corey Wayne Speers ◽  
S. Laura Chang ◽  
Benjamin Chandler ◽  
Andrea Pesch ◽  
Anna Michmerhuizen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cox Regression ◽  
External Validation ◽  
Rank Correlation ◽  
Local Therapy ◽  
Feature Reduction ◽  
Recurrence Time ◽  
P Value ◽  
Treatment Intensification

112 Background: Unmet clinical needs in breast cancer (BC) management include the identification of patients at high risk to fail locally despite standard local therapy and an understanding of the biology of these recurrences. We previously reported a radiation response signature and here extend those studies to identify a signature predictive of timing of recurrence after RT. Methods: 2 independent patient cohorts were used for training (119 pts) and validation (112 pts). All patients received RT after BCS and systemic therapy as appropriate. Spearman’s rank correlation to correlate gene expression to recurrence time was used for feature selection. Significant genes were used to train a linear model which was locked before validation. Cox regression was used for both UVA and MVA. Results: Spearman’s correlation identified 485 genes whose expression was significantly associated with recurrence time (+/-3 yrs). Feature reduction refined the list to 41 genes retained within the signature. In training, the correlation of score to recurrence time was 0.85, p-value < 1.3x10-31; AUC of 0.91. External validation in an independent BC validation set accurately identified patients with early vs. late recurrences (correlation= 0.75, p-value = 0.001, AUC = 0.92, sens.=0.75, spec.= 1.0, PPV = 1.0, NPV = 0.8). Unique associations of breast cancer intrinsic subtype to timing of local recurrence were found. In UVA and MVA the signature remained the most significant factor associated with recurrence. GSEA analysis of the 41 genes retained within the signature identified proliferation and EGFR concepts associated with early recurrences and luminal and ER-signaling pathways associated with late recurrences. Knockdown of genes associated with the early and late recurrences demonstrated novel effects on proliferation and clonogenic survival, respectively. Conclusions: We report a BC gene expression signatures that may be useful in identifying patients unlikely to respond to adjuvant RT and may be used to predict timing of recurrences, with implications for potential treatment intensification and duration of follow-up for women with breast cancer treated with RT.

scholarly journals Memory and Spatial Cognition in Breast Cancer Patients Undergoing Adjuvant Endocrine Therapy

Breast Care ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. 240-246 ◽  
Author(s):  
Ute Berndt ◽  
Bernd Leplow ◽  
Robby Schoenfeld ◽  
Tilmann Lantzsch ◽  
Regina Grosse ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Spatial Cognition ◽  
Verbal Memory ◽  
Multiple Testing ◽  
Local Therapy ◽  
Protective Role ◽  
P Value ◽  
Breast Cancer Patients ◽  
Cross Sectional ◽  
Switch Group

Introduction: It is generally accepted that estrogens play a protective role in cognitive function. Therefore, it can be expected that subtotal estrogen deprivation following aromatase inhibition will alter cognitive performance. Methods: In a cross-sectional study we investigated 80 postmenopausal women with breast cancer. Memory and spatial cognition were compared across 4 treatment groups: tamoxifen only (TAM, n = 22), aromatase inhibitor only (AI, n = 22), TAM followed by AI (‘SWITCH group', n = 15), and patients with local therapy (LT) only (surgery and radiation, n = 21). Duration of the 2 endocrine monotherapy arms prior to the assessment ranged from 1 to 3 years. The ‘SWITCH group' received 2-3 years TAM followed by at least 1 year and at most 3 years of AI. Memory and spatial cognition were investigated as planned comparisons. Investigations of processing speed, attention, executive function, visuoconstruction and self-perception of memory were exploratory. Results: With regard to general memory, AI patients performed significantly worse than the LT group (p = 0.013). Significant differences in verbal memory did not remain significant after p-value correction for multiple testing. We found no significant differences concerning spatial cognition between the groups. Conclusion: AI treatment alone significantly impairs general memory compared to the LT group.

scholarly journals Identification of a Novel Gene Model-Based Homologous Recombination Deficiency Score to Improve Survival Prediction of TNBC.

2021 ◽  
Author(s):  
Wenxiang Zhang ◽  
Bolun Ai ◽  
Xiangyi Kong ◽  
Xiangyu Wang ◽  
Jie Zhai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Homologous Recombination ◽  
Risk Score ◽  
Cox Regression ◽  
Roc Curves ◽  
Cox Regression Analysis ◽  
Homologous Recombination Deficiency ◽  
Ajcc Stage ◽  
Kaplan Meier

Abstract Background Triple-negative breast cancer (TNBC) is a specific histological type of breast cancer with a poor prognosis, early recurrence, which lacks durable chemotherapy responses and effective targeted therapies. We aimed to construct an accurate prognostic risk model based on homologous recombination deficiency (HRD) - gene expression profiles for improving prognosis prediction of TNBC. Methods Triple-negative breast cancer RNA sequencing data and sample clinical information were downloaded from the breast invasive carcinoma (BRCA) cohort in the Cancer Genome Atlas (TCGA) database. Combined with the HRD database, tumor samples were divided into two sets. We screened differentially expressed genes (DEGs) and then identified HRD-related prognostic genes using weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identifying key prognostic genes. Risk scores were calculated and compared with HRD score, Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE103091 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to independently verify the prognosis of the risk score. A nomogram was constructed and revealed by time-dependent ROC curves to guide clinical practice. Results We found that HRD tumor samples (HRD score > = 42) in TNBC patients were associated with poor overall survival (p = 0.027). We identified a total of 147 differential genes including 203 up-regulated and 213 down-regulated genes, among which 29 were prognosis-related genes. Through the LASSO method, 6 key prognostic genes ((MUCL1, IVL, FAM46C, CHI3L1, PRR15L, and CLEC3A) were selected and a 6-gene risk score was constructed. We found risk score was negatively associated with homologous recombination deficiency (HRD) scores (r = -0.22, p = 0.019). Compared with the low-risk group, Kaplan-Meier survival analysis shows that the high-risk group has an obvious poorer prognosis (P < 0.0001). Finally, we integrated the risk score model and clinical factors of TNBC (AJCC-stage, HRD score, T stage, and N stage) to construct a compound nomogram. Time-dependent ROC curves showed the risk score performed better in 1-, 3- and 5-year survival predictions compared with AJCC-stage. Conclusions Based on HRD gene expression data, our six HRD-related gene signature and nomogram could be practical and reliable tools for predicting OS in patients with TNBC.

scholarly journals Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yun Zhong ◽  
Zhe Liu ◽  
Dangchi Li ◽  
Qinyuan Liao ◽  
Jingao Li
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Test Set ◽  
Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

Cytokeratin-19 (CK19) and mammaglobin (MGB1) gene expression in circulating tumor cells (CTCs) from metastatic breast cancer patients enrolled in the NCCTG trials, N0436 and N0437

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 11095-11095
Author(s):  
M. M. Reinholz ◽  
K. A. Kitzmann ◽  
T. J. Hobday ◽  
D. W. Northfelt ◽  
B. LaPlant ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Metastatic Breast Cancer ◽  
Treatment Response ◽  
Cox Regression ◽  
Imaging System ◽  
Metastatic Breast ◽  
Cytokeratin 19 ◽  
Mrna Levels ◽  
Ck19 Mrna

11095 Background: Biologic characterization of CTCs is increasingly important in determining metastatic breast cancer (MBC) patient (pt) prognosis and treatment prediction. Combined preliminary results from two earlier metastatic BC NCCTG trials, N0234 & N0336, suggested that the change in CTC mammaglobin (MGB1) gene expression between baseline and two cycles of chemotherapy predicted tumor response (p=0.04). The objectives of this study were to 1) determine CTC gene expression of CK19 and MGB1 before, during, and after treatment in N0436 & N0437 and 2) determine associations between baseline and post-treatment gene expression and treatment response. Methods: CTCs were enriched using CD45-depletion from ∼10ml EDTA blood obtained from metastatic BC pts before, after two cycles, and at end of treatment with either first/second-line irinotecan plus cetuximab (N0436) or first-line paclitaxel poliglumex and capecitabine (N0437). CK19 and MGB1 mRNA levels were determined using quantitative RT-PCR in baseline and serial CTC samples of up to 19 pts from N0436 and 40 pts from N0437. The relative gene expressions were normalized to β2-microglobulin and calibrated to healthy blood using the 2-ΔΔCt algorithm; a value of 2 was defined as positive for the respective marker. Results: CK19+ mRNA was detected in 58% of baseline samples from N0436 (11/19) and N0437 (23/40). MGB1+ mRNA was detected in 32% (6/19) and 38% (15/40) of N0436 and N0437 baseline samples, respectively. CK19+ mRNA was detected in 50% (7/14) and 56% (29/52) of N0436 and N0437 serial CTC samples, respectively. MGB1+ mRNA was detected in 29% (4/14) and 27% (14/52) of N0436 and N0437 serial CTC samples, respectively. Of the 66 serial samples, 27% of samples (18/66) had turned positive from baseline for CK19 or MGB1. CK19 mRNA was detected in 85% (33/39) of MGB1+ mRNA samples but their baseline mRNA levels were not correlated. Conclusions: CK19 mRNA was detected in MBC pts with similar frequencies to the CellSearch imaging system. CK19 was detected at a higher frequency than MGB1. In the majority of cases, MGB1 was co-expressed with CK19. Associations between gene expression and treatment response using Chi-Squared analyses and Cox regression models will be presented. No significant financial relationships to disclose.

Association between stromal/TGF-β/EMT gene expression signature and response to pembrolizumab monotherapy in cisplatin-ineligible patients with locally advanced (unresectable) or metastatic urothelial carcinoma.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 433-433 ◽  
Author(s):  
Petros Grivas ◽  
Daniel E. Castellano ◽  
Peter H. O'Donnell ◽  
Razvan Cristescu ◽  
Tara L. Frenkl ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Urothelial Carcinoma ◽  
Cox Regression ◽  
Performance Status ◽  
Locally Advanced ◽  
Gene Expression Signature ◽  
P Value ◽  
Expression Signature ◽  
Ecog Ps

433 Background: PD-L1 immunohistochemistry and an 18-gene T cell–inflamed gene expression profile (GEP) are associated with response to anti–PD-1/PD-L1 therapy across tumor types, including urothelial carcinoma. A gene expression signature representing convergent biology related to stromal/EMT/TGF-β pathways was developed and prespecified for testing for association with pembrolizumab response in urothelial carcinoma patients treated on the KEYNOTE-052 trial (NCT02335424). Methods: KEYNOTE-052 was a single-arm phase 2 trial of pembrolizumab in cisplatin-ineligible patients with previously untreated, advanced urothelial carcinoma. Primary objective of this analysis was to assess the association between the Stromal/EMT/TGF-β signature and outcomes (best overall response [BOR], PFS, OS) as an independent biomarker and to understand its potential prognostic/predictive role beyond the T cell–inflamed GEP score or PD-L1 assessed using combined positive score (CPS). Cox regression models for PFS and OS and a logistic regression model for BOR evaluated associations between Stromal/EMT/TGF-β signature and outcomes adjusting for ECOG performance status (PS) and level of the GEP or CPS (1-sided P value). Results: RNA-Seq data from baseline tumor specimens were available for 187/370 patients on KEYNOTE-052. Lower Stromal/EMT/TGF-β score was associated with favorable BOR rate ( P < 0.001), PFS ( P < 0.001), and OS ( P = 0.002) after adjustment for ECOG PS and GEP (which remained significant at the 0.05 level in all cases). The patterns indicated a very consistent downward trend in the distribution of the Stromal/EMT/TGF-β score for responders versus nonresponders, regardless of GEP. In models that adjusted for both ECOG PS and PD-L1 CPS, the Stromal/EMT/TGF-β score remained significant (BOR rate, P = 0.002; PFS, P = 0.013; OS, P = 0.029). Conclusions: Higher Stromal/EMT/TGF-β signature was associated with resistance to pembrolizumab independently of GEP or PD-L1 in urothelial carcinoma patients on the KEYNOTE-052 trial. Clinical trial information: NCT02335424.

scholarly journals High Expression of microRNA-223 Indicates a Good Prognosis in Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Li Chen ◽  
Xiuzhi Zhu ◽  
Boyue Han ◽  
Lei Ji ◽  
Ling Yao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Molecular Subtype ◽  
External Validation ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Locked Nucleic Acid ◽  
Tnbc Subtype

PurposeMicroRNAs can influence many biological processes and have shown promise as cancer biomarkers. Few studies have focused on the expression of microRNA-223 (miR-223) and its precise role in breast cancer (BC). We aimed to examine the expression level of miR-223 and its prognostic value in BC.MethodsTissue microarray (TMA)-based miRNA detection in situ hybridization (ISH) with a locked nucleic acid (LNA) probe was used to detect miR-223 expression in 450 BC tissue samples. Overall survival (OS) and disease-free survival (DFS) were compared between two groups using the Kaplan-Meier method and Cox regression model.ResultsOS and DFS were prolonged in the high miR-223 expression group compared to the low miR-223 expression group (p &lt; 0.0001 and p = 0.017, respectively), especially in patients with the triple-negative breast cancer (TNBC) subtype (p = 0.046 and p &lt; 0.001, respectively). Univariate and multivariate Cox regression analyses revealed that TNM stage (p = 0.008), the molecular subtype (p = 0.049), and miR-223 (p &lt; 0.001) were independently associated with OS and DFS. External validation was performed with the METABRIC and The Cancer Genome Atlas (TCGA) databases via online webtools and was consistent with the data described above.ConclusionsThis study provides evidence that high miR-223 expression at diagnosis is associated with improved DFS and OS for BC patients, especially those with the TNBC subtype. miR-223 is a valid and independent prognostic biomarker in BC.

scholarly journals A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

2020 ◽  
Author(s):  
Xing Chen ◽  
Junjie Zheng ◽  
Min ling Zhuo ◽  
Ailong Zhang ◽  
Zhenhui You
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. 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 tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

scholarly journals Association Analysis of Deep Genomic Features Extracted by Denoising Autoencoders in Breast Cancer

Cancers ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 494 ◽  
Author(s):  
Qian Liu ◽  
Pingzhao Hu
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Characteristics ◽  
Genomic Data ◽  
Risk Scores ◽  
P Value ◽  
Positive Regulation ◽  
Genomic Features ◽  
Survival Differences ◽  
Breast Cancer Gene

Artificial intelligence-based unsupervised deep learning (DL) is widely used to mine multimodal big data. However, there are few applications of this technology to cancer genomics. We aim to develop DL models to extract deep features from the breast cancer gene expression data and copy number alteration (CNA) data separately and jointly. We hypothesize that the deep features are associated with patients’ clinical characteristics and outcomes. Two unsupervised denoising autoencoders (DAs) were developed to extract deep features from TCGA (The Cancer Genome Atlas) breast cancer gene expression and CNA data separately and jointly. A heat map was used to view and cluster patients into subgroups based on these DL features. Fisher’s exact test and Pearson’ Chi-square test were applied to test the associations of patients’ groups and clinical information. Survival differences between the groups were evaluated by Kaplan–Meier (KM) curves. Associations between each of the features and patient’s overall survival were assessed using Cox’s proportional hazards (COX-PH) model and a risk score for each feature set from the different omics data sets was generated from the survival regression coefficients. The risk scores for each feature set were binarized into high- and low-risk patient groups to evaluate survival differences using KM curves. Furthermore, the risk scores were traced back to their gene level DAs weights so that the three gene lists for each of the genomic data points were generated to perform gene set enrichment analysis. Patients were clustered into two groups based on concatenated features from the gene expression and CNA data and these two groups showed different overall survival rates (p-value = 0.049) and different ER (Estrogen receptor) statuses (p-value = 0.002, OR (odds ratio) = 0.626). All the risk scores from the gene expression and CNA data and their concatenated one were significantly associated with breast cancer survival. The patients with the high-risk group were significantly associated with patients’ worse outcomes (p-values ≤ 0.0023). The concatenated risk score was enriched by the AMP-activated protein kinase (AMPK) signaling pathway, the regulation of DNA-templated transcription, the regulation of nucleic acid-templated transcription, the regulation of apoptotic process, the positive regulation of gene expression, the positive regulation of cell proliferation, heart morphogenesis, the regulation of cellular macromolecule biosynthetic process, with FDR (false discovery rate) less than 0.05. We confirmed DAs can effectively extract meaningful genomic features from genomic data and concatenating multiple data sources can improve the significance of the features associated with breast cancer patients’ clinical characteristics and outcomes.

Metastatic breast cancer and the elderly: A single institution, retrospective review.

2012 ◽  
Vol 30 (27_suppl) ◽  
pp. 55-55
Author(s):  
Michael Kidd ◽  
Nina J. Karlin ◽  
Amylou C. Dueck
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Cox Regression ◽  
Age Groups ◽  
Metastatic Breast ◽  
Age At Diagnosis ◽  
Her2 Status ◽  
P Value ◽  
Cox Regression Analysis ◽  
Hormone Status

55 Background: The aim of this retrospective study was to examine the overall survival (OS) of metastatic breast cancer patients over a decade and assess for any differences with respect to age, tumor characteristics, and ECOG status. Methods: Data on metastatic breast cancer cases from 1999-2010 were retrieved from the institutional cancer registry and linked to electronic medical records. Through chart review of 240 metastatic breast cancer cases, we determined hormone receptor (HR), HER2/neu (HER2), ECOG, age at diagnosis of metastatic cancer and mortality data. Kaplan-Meier survival curves for OS were used and compared between HR status, HER2 status, ECOG, and age at diagnosis using log-rank regression and Cox Regression analysis was performed to control for these variables. A 95% confidence interval (CI) was used. Results: The median OS of the sampled cases was 2.2 years (CI: 1.8-2.5 years). Analysis for overall survival by pre-determined age groups demonstrated no significant difference between the age groups (p value 0.46), but did yield a statistical difference based on ECOG status (p value 0.0001). Cox regression analysis showed consistent findings where survival was significantly affected by HR, HER2 status and ECOG but not age (HR: p value <0.0001; HER2: p value 0.0132; ECOG: p value <0.0001; age at diagnosis: p value 0.8462). Analysis for OS based on HR yielded a median survival of 1.4 years (CI: 0.9-1.6 years) for HR negative and 2.5 years (CI: 2.2-3.0 years) for HR positive (p value 0.0018). HER2 yielded a median survival of 1.8 years (CI: 1.4-2.3 years) for no amplification and 3.0 years (CI: 2.5 - 3.4 years) for amplification (p-value 0.0043). HR negative, HER2 non-amplified tumors had the poorest median OS at 0.7 years (CI: 0.5 - 1.1 years) whereas those tumors with HR positive, HER2 amplified had the best median OS at 3.0 years (CI: 2.5 - 4.7 years) with a p value < 0.0001. Conclusions: In this retrospective analysis, there was no significant survival difference with respect to age. Age continued to have no significant effect on survival when adjusting for ECOG, hormone status, and HER2/neu status. Those factors that did act as determinants of survival were ECOG status, hormone status and HER2/neu status of the tumor.

Predictors of adjuvant radiation therapy in the treatment of male breast cancer and its impact on outcomes: A propensity matched analysis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e12516-e12516
Author(s):  
Veli Bakalov ◽  
Thejus Thayyil Jayakrishnan ◽  
Stephen Abel ◽  
Christie Hilton ◽  
Bindu Rusia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Radiation Therapy ◽  
Male Breast Cancer ◽  
Cox Regression ◽  
Breast Conservation ◽  
Male Breast ◽  
Breast Conservation Surgery ◽  
P Value ◽  
Adjuvant Radiation ◽  
Adjuvant Radiation Therapy

e12516 Background: Male breast cancer (MBC) accounts for 1% of all breast cancers and there is a paucity of data on factors impacting the treatment strategies and outcomes. We hypothesized that adjuvant radiation therapy (Adj-RT) may improve survival outcomes and sought to examine predictive factors for Adj-RT receipt. Methods: We queried the National Cancer Database (NCDB) for patients with stages I-III MBC treated with surgery (breast conservation surgery- BCS or mastectomy-MS) within 180 days of diagnosis (years 2004-2015). Multivariable logistic regression identified predictors of adjuvant radiation therapy receipt. Multivariable Cox regression evaluated predictors of survival. Propensity matching for adj-RT accounted for indication biases. Results: We identified 6,217 patients meeting the eligibility criteria (1457 BCS vs. 4760 MS). The majority of patients were white (85%) and within the age range of 50-80 years (74%). Although Adj-RT was omitted for 30% of BCS patients, the utilization was higher compared to MS (OR=26, p-value=0.001). The predictors of Adj-RT use were – African American race, higher stage, higher grade, presence of lymphovascular invasion and ER/Her-2 positivity for the entire cohort and higher age, urban location and higher income for BCS. Adj-RT was associated with lower mortality in the propensity matched model (overall HR for BCS=0.28, p-value<0.001; overall HR for MS=0.62,p-value=0.001) and is shown in the table. Conclusions: This study demonstrates there may be an association between decreased mortality and Adj-RT in MBC undergoing BCS. Although this implies that Adj-RT should be routinely delivered, it appears to be omitted frequently and its use requires further investigation. The study also suggests a benefit to Adj-Rt after MS for stage-III MBC. [Table: see text]

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