scholarly journals A Systemic Inflammation Response Score for Prognostic Prediction of Breast Cancer Patients Undergoing Surgery

2021 ◽  
Vol 11 (5) ◽  
pp. 413
Author(s):  
Kaiming Zhang ◽  
Liqin Ping ◽  
Xueqi Ou ◽  
Meiheban Bazhabayi ◽  
Xiangsheng Xiao
Keyword(s):  
Breast Cancer ◽  
Receiver Operating Characteristic ◽  
Cancer Patients ◽  
Clinical Features ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Curve Analysis ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Response Score

Background: Systemic inflammatory response is related to the occurrence, progression, and prognosis of cancers. In this research, a novel systemic inflammation response score (SIRS) was calculated, and its prognostic value for postoperative stage I-III breast cancer (BC) patients was analyzed. Methods: 1583 BC patients were included in this research. Patients were randomly divided into a training cohort (n = 1187) and validation cohort (n = 396). SIRS was established in the training cohort based on independent prognostic hematological indicator, its relationship between prognosis and clinical features was analyzed. Then, a nomogram consisted of SIRS and clinical features was established, its performance was examined by calibration plots and receiver operating characteristic curve analysis. Results: The SIRS was an independent prognostic indicator for BC patients, and a high-SIRS was related to multifocality, advanced N stage, and worse prognosis. Incorporating SIRS into a nomogram could accurately predict the prognosis of BC patients, the results of receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of nomogram was up to 0.806 in training cohort and 0.905 in the validation cohort. Conclusion: SIRS was associated with the prognosis of patients with breast cancer. Nomogram based on SIRS can accurately predict the prognosis of breast cancer patients.

Integrated immune-related gene signature to predict clinical outcome for patients with luminal B breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12526-e12526
Author(s):  
Xiaying Kuang ◽  
Du Cai ◽  
Ying Lin ◽  
Feng Gao
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Luminal B ◽  
Immune Related Gene ◽  
Immune Related Genes

e12526 Background: Luminal B breast cancer is always routinely treated with chemotherapy and endocrine therapy but heterogeneous with respect to sensitivity to treatment, identification of patients who may most benefit remains a matter of controversy. Immune-related genes (IRGs) was found to be associated with the prognosis of breast cancer. The aim of this study is to evaluate the impact of IRGs in predicting the outcome of luminal B breast cancer patients. Methods: According to the Metabric microarray dataset also as a training cohort, 488 luminal B breast cancer patients were selected for generation of immune-related gene signature (IRGS). Another independent dataset (n=250) of patients with complete prognostic information was analyzed as a validation cohort. Prognostic analysis was assessed to test the predictive value of IRGS. Results: A model of prognostic IRGS containing 12 immune-related genes was developed. In both training and validation cohorts, IRGS significantly stratified luminal B breast cancer patients into immune low- and high-risk groups in terms of disease free survival (DFS, HR=4.95, 95% CI=3.22-7.62, P<0.001 in training cohort, HR=2.47, 95% CI=1.29-4.75, P<0.001 in validation cohort). Multivariate analysis revealed IRGS as an independent prognostic factor (HR=4.96, 95% CI=3.00-8.18, P<0.001 in training cohort, HR=2.56, 95% CI=1.28-5.09, P=0.007 in validation cohort). Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. Conclusions: The proposed IRGS is a satisfactory prognostic model for estimating DFS of luminal B breast cancer patients. Further studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for luminal B breast cancer patients. This work was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596).

scholarly journals Construction and Validation of a Serum Albumin-to-Alkaline Phosphatase Ratio-Based Nomogram for Predicting Pathological Complete Response in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fanli Qu ◽  
Zongyan Li ◽  
Shengqing Lai ◽  
XiaoFang Zhong ◽  
Xiaoyan Fu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Alkaline Phosphatase ◽  
Cancer Patients ◽  
Predictive Value ◽  
Validation Cohort ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Pretreatment Serum

BackgroundBreast cancer patients who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) have favorable outcomes. Reliable predictors for pCR help to identify patients who will benefit most from NAC. The pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) has been shown to be a prognostic predictor in several malignancies, but its predictive value for pCR in breast cancer is still unknown. This study aims to investigate the predictive role of AAPR in breast cancer patients and develop an AAPR-based nomogram for pCR rate prediction.MethodsA total of 780 patients who received anthracycline and taxane-based NAC from January 2012 to March 2018 were retrospectively analyzed. Univariate and multivariate analyses were performed to assess the predictive value of AAPR and other clinicopathological factors. A nomogram was developed and calibrated based on multivariate logistic regression. A validation cohort of 234 patients was utilized to further validate the predictive performance of the model. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.ResultsPatients with a lower AAPR (&lt;0.583) had a significantly reduced pCR rate (OR 2.228, 95% CI 1.246-3.986, p=0.007). Tumor size, clinical nodal status, histological grade, PR, Ki67 and AAPR were identified as independent predictors and included in the final model. The nomogram was used as a graphical representation of the model. The nomogram had satisfactory calibration and discrimination in both the training cohort and validation cohort (the C-index was 0.792 in the training cohort and 0.790 in the validation cohort). Furthermore, DCA indicated a clinical net benefit from the nomogram.ConclusionsPretreatment serum AAPR is a potentially valuable predictor for pCR in breast cancer patients who receive NAC. The AAPR-based nomogram is a noninvasive tool with favorable predictive accuracy for pCR, which helps to make individualized treatment strategy decisions.

Development and validation of novel microenvironment-based immune molecular subtypes of breast cancer: Implications for immunotherapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 1094-1094
Author(s):  
Yunfang Yu ◽  
Wenda Zhang ◽  
Qiyun Ou ◽  
Anlin Li ◽  
Yongjian Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Molecular Subtypes ◽  
Immune Gene ◽  
Inverse Association ◽  
Breast Cancer Patients ◽  
Data Set ◽  
Training Cohort ◽  
Ifn Γ

1094 Background: Breast cancer treatment with immunotherapy can improve clinical benefits, but the majority of patients did not respond to the treatment. To understand tumor–immune interactions in breast cancer, we identified novel microenvironment-based immune molecular subtypes. Methods: A training cohort of 1,394 breast cancer patients from the Molecular Taxonomy of Breast Cancer International Consortium profiled by RNA and DNA sequencing data were analyzed to calculate immune-related gene biomarkers and to assign prognostic categories using LASSO Cox regression model. Additionally, 969 patients from The Cancer Genome Atlas data set was used as an independent validation cohort. We further compared tumor mutation burden (TMB) and cytolytic activity (CYT) levels between different immune molecular subtypes. Results: Using the LASSO model, we established an immune molecular classifier based on following 5 features: IFN-γ signature, ICOSLG, TNFRSF14, Mast.cells.resting, and T.cells. CD4.memory.resting. Then we found that it contained two distinct microenvironment-based subtypes (immune class and non-immune class), characterized by significant differences in overall survival in the training cohort (hazard ratio [HR] 0.71; 95% confidence interval [CI] 0.61 to 0.81; P < 0.001) and in the validation cohort (HR 0.34; 95% CI 0.22 to 0.54; P < 0.001). We found an inverse association between immune gene expression and TMB levels (ρ = 0.096, P < 0.001). Immune class subtype patients with good prognosis had significantly lower TMB and higher CYT than did non-immune class subtype patients with poor prognosis (all, P < 0.05). The clinical use of the immune molecular subtypes showed a closer association with survival than did IFN-γ signature or PD-L1 expression (all, P < 0.05). The robustness of the immune molecular subtypes was confirmed in the validation cohort. Conclusions: We revealed novel immune molecular subtypes, which represented better utility in predicting breast cancer patients’ survival compared with IFN-γ signature or PD-L1, and could be an important guide for precision immunotherapy.

scholarly journals Plasma Extracellular Vesicle Long RNA Profiles in the Diagnosis and Prediction of Treatment Response for Breast Cancer

2020 ◽  
Author(s):  
Yonghui Su ◽  
Jingjing Zhao ◽  
Rong Guo ◽  
Hongyan Lai ◽  
Weiru Chi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Treatment Efficacy ◽  
Validation Cohort ◽  
Extracellular Vesicle ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Efficacy Prediction

Abstract Background: The utility of extracellular vesicle long RNAs (exLRs) as noninvasive biomarkers in breast cancer remains elusive. The purpose of this study was to explore the potential of exLRs as clinically actionable biomarkers for breast cancer diagnosis, classification, and neoadjuvant therapy efficacy prediction. Methods: One hundred and seventy-two participants, including 112 breast cancer patients, 19 benign patients and 41 healthy controls, were enrolled in this case-control study. The exLR profile of the plasma samples was analyzed by exLR sequencing. The d-signature was identified using a support vector machine algorithm with a training cohort (n=120) and was validated using an internal validation cohort (n=52). Treatment efficacy prediction was conducted with 48 patients who received neoadjuvant chemotherapy.Results: We constructed a breast cancer diagnostic signature that showed high accuracy with an area under the curve (AUC) of 0.960 in the training cohort and 0.900 in the validation cohort. The signature was able to identify early stage BC (I/II) with an AUC of 0.940. Integrating the signature could increase the diagnosis accuracy by up to 91.9% for breast cancer patients with the corresponding predictive results based on the Breast Imaging Reporting and Data System classification of 4 or 5. Moreover, the exLRs could provide a strong indication of the breast cancer subtypes, and exMSMO1 is employable as a predictive biomarker in response to neoadjuvant chemotherapy.Conclusions: This study demonstrated the value of exLR profiling to provide potential biomarkers for early detection and treatment efficacy prediction of breast cancer.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

Serum ANGPTL2 Levels Reflect Clinical Features of Breast Cancer Patients: Implications for the Pathogenesis of Breast Cancer Metastasis

2014 ◽  
Vol 29 (3) ◽  
pp. 239-245 ◽  
Author(s):  
Motoyoshi Endo ◽  
Yutaka Yamamoto ◽  
Masahiro Nakano ◽  
Tetsuro Masuda ◽  
Haruki Odagiri ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Clinical Features ◽  
Cancer Progression ◽  
Breast Cancer Cells ◽  
Cancer Metastasis ◽  
Ductal Carcinoma ◽  
Breast Cancer Metastasis ◽  
Breast Cancer Patients

Introduction Breast cancer is a leading cause of cancer-related death in women worldwide, and its metastasis is a major cause of disease mortality. Therefore, identification of the mechanisms underlying breast cancer metastasis is crucial for the development of therapeutic and diagnostic strategies. Our recent study of immunodeficient female mice transplanted with MDA-MB231 breast cancer cells demonstrated that tumor cell-derived angiopoietin-like protein 2 (ANGPTL2) accelerates metastasis through both increasing tumor cell migration in an autocrine/paracrine manner, and enhancing tumor angiogenesis. To determine whether ANGPTL2 contributes to its clinical pathogenesis, we asked whether serum ANGPTL2 levels reflect the clinical features of breast cancer progression. Methods We monitored the levels of secreted ANGPTL2 in supernatants of cultured proliferating MDA-MB231 cells. We also determined whether the circulating ANGPTL2 levels were positively correlated with cancer progression in an in vivo breast cancer xenograft model using MDA-MB231 cells. Finally, we investigated whether serum ANGPTL2 levels were associated with clinical features in breast cancer patients. Results Both in vitro and in vivo experiments showed that the levels of ANGPTL2 secreted from breast cancer cells increased with cell proliferation and cancer progression. Serum ANGPTL2 levels in patients with metastatic breast cancer were significantly higher than those in healthy subjects or in patients with ductal carcinoma in situ or non-metastatic invasive ductal carcinoma. Serum ANGPTL2 levels in patients negative for estrogen receptors and progesterone receptors, particularly triple-negative cases, reflected histological grades. Conclusions These findings suggest that serum ANGPTL2 levels in breast cancer patients could represent a potential marker of breast cancer metastasis.

Clinical features and prognosis of obese breast cancer patients: a retrospective study

2013 ◽  
Vol 12 (9) ◽  
pp. 411-415
Author(s):  
Zhendong Zheng ◽  
Heng Cao ◽  
Shuxian Qu ◽  
Yongye Liu ◽  
Ying Piao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Retrospective Study ◽  
Cancer Patients ◽  
Clinical Features ◽  
Breast Cancer Patients

scholarly journals Prediction of distant metastasis of spinal and pelvic osteosarcoma by constructing and validating nomogram:a retrospective study based on SEER database

2022 ◽  
Author(s):  
Yu Lin ◽  
Zhenyu Wang ◽  
Gang Chen ◽  
Wenge Liu
Keyword(s):  
Risk Factor ◽  
Receiver Operating Characteristic ◽  
Survival Time ◽  
Distant Metastasis ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Seer Database ◽  
Lasso Regression ◽  
Training Cohort ◽  
Kaplan Meier

Abstract Background:Spinal and pelvic osteosarcoma is a rare type of all osteosarcomas,and distant metastasis is an important factor for poor prognosis of this disease. There are no similar studies on prediction of distant metastasis of spinal and pelvic osteosarcoma. We aim to construct and validate a nomogram to predict the risk of distant metastasis of spinal and pelvic osteosarcoma.Methods:We collected the data on patients with spinal and pelvic osteosarcoma from the Surveillance, Epidemiology, and End Results(SEER) database retrospectively. The Kaplan-Meier curve was used to compare differences in survival time between patients with metastasis and non-metastasis. Total patients were randomly divided into training cohort and validation cohort. The risk factor of distant metastasis were identified via the least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic analysis. The nomogram we constructed were validated internally and externally by C-index, calibration curves,receiver operating characteristic(ROC) curve and Decision curve analysis (DCA).Results:The Kaplan-Meier curve showed that the survival time of non-metastatic patients was longer than that of metastatic patients(P＜0.001).All patients(n=358) were divided into training cohort(n=269) and validation cohort(n=89).The LASSO regression selected five meaningful variables in the training cohort. The multivariate logistic regression analysis demonstrated that surgery(yes,OR=0.175, 95%CI=0.095-0.321,p=0.000) was the independent risk factors for distant metastasis of patients with spinal and pelvic osteosarcoma. The C-index and calibration curves showed the good agreement between the predicted results and the actual results. The area under the receiver operating characteristic curve(AUC) values were 0.748(95%CI=0.687-0.817) and 0.758(95%CI=0.631-0.868) in the training and validation cohorts respectively. The DCA showed that the nomogram has a good clinical usefulness and net benefit.Conclusion:No surgery is the independent risk factor of distant metastasis of spinal and pelvic osteosarcoma. The nomogram we constructed to predict the probability of distant metastasis of patients with spinal and pelvic osteosarcoma is reliable and effective by internal and external verification.

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