scholarly journals Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Gangqin Xi ◽  
Lida Qiu ◽  
Shuoyu Xu ◽  
Wenhui Guo ◽  
Fangmeng Fu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Proportional Hazard ◽  
Breast Cancer Patients ◽  
Cox Proportional Hazard ◽  
Clinical Center ◽  
Microscopic Features ◽  
Cox Proportional Hazard Regression

Abstract Background Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients. Methods In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS). Results TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873–0.938]; 0.896, [0.860–0.931]; 0.882, [0.840–0.925] in the three cohorts). Conclusion These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.

Persistence of circulating tumour cells after treatment predicts clinical outcome in breast cancer patients

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21080-21080
Author(s):  
M. Serrano ◽  
P. Sánchez-Rovira ◽  
M. Campos ◽  
F. Warleta ◽  
J. Ruiz-Mora ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Clinical Outcome ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Proportional Hazard ◽  
Breast Cancer Patients ◽  
Cox Proportional Hazard ◽  
Cox Proportional Hazard Regression

21080 Background: The hematogenous distant metastasis is the leading cause of cancer death. This process involves the passage through the blood and lymphatic circulation of circulating tumor cells (CTC) with metastatic properties. Thus, the early detection of such cells has important implication for cancer prognosis, monitoring of treatment and to predict clinical outcome. We present the results that showed that CTC are prognostic factor after chemotherapy independently of treatment. Methods: A total of 59 patients with breast cancer were enrolled in this study between April 2000 and December 2002. The median follow-up was 50 months. All patients received chemotherapy as first line treatment. Results of this work included CTC detection one month after of the end of chemotherapy. After informed consent, 10ml of heparinized peripheral blood was collected from patients. For enrichment of CTC we use the Carcinoma Cell Enrichment and Detection kit using MACS technology (Miltenyi Biotec). After enrichment of epithelial tumor cells immunomagnetic labeled with a multi- cytokeratin-specific antibody, the positive cells were detected by immunocytochemical staining with alkaline phosphatase substrate. Results: Analysis of CTC after chemotherapy: Circulating tumor cells were detected in 32 patients (54.23%). A mean number of cells were detected 3.7 (SD 13.9; range 1–105).The number of CTC was correlationed with progression free of disease (PFS) and overall survival (OS). In the univariate Cox proportional hazard regression analysis number cells were significantly associated with OS (p = 0.020) and PFS (p = 0.008). In the multivariate Cox proportional hazard regression analysis, number cells admit borderline statically significance with PFS (p = 0.052) and OS (p = 0.071). Conclusions: Our results suggest that the persistence of CTC after the treatment predicts clinical outcome and therefore the detection of CTC in breast cancer patients might allow monitoring of chemotherapy response. No significant financial relationships to disclose.

scholarly journals Postmastectomy Radiation Therapy can Improve Survival for Breast Cancer Patients with 1-3 Positive Axillary Lymph Nodes: A Retrospective Cohort Study Using SEER Database

2020 ◽  
Author(s):  
Jian Yang ◽  
Xiao Zhang ◽  
Yifeng Ye ◽  
qingmo yang ◽  
Haoyang Cai
Keyword(s):  
Breast Cancer ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Subgroup Analysis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Proportional Hazard ◽  
Breast Cancer Patients ◽  
Cox Proportional Hazard ◽  
Postmastectomy Radiation

Abstract Postmastectomy radiation (PMRT) is an important adjuvant treatment for high-risk breast cancer. However, evidence concerning the efficacy of PMRT on survival for breast cancer patients with 1-3 positive axillary lymph nodes remains insufficient. We identified 57,793 patients from the Surveillance, Epidemiology, and End Results database, including 15,126 cases of beam radiation and 42,667 cases of none/unknown radiation. All patients were diagnosed during 2010–2015. Kaplan–Meier curve was utilized to compare the survival of the two groups. We used univariate and multivariate Cox proportional hazard models to identify independent prognostic factors with Hazard Ratio and 95% Confidence Intervals. Patients were stratified according to lymph node status, tumor size and molecular subtypes to perform subgroup analysis. The PMRT group shows more aggressive clinicopathological features, including higher grade (p<0.001), lager tumor size (p<0.001), more lymph nodes (p<0.001), younger age (p<0.001), more ER absence (p<0.001), more PR absence (p<0.001), and more HER2 overexpression (p<0.001). In addition, the PMRT group received more radical surgery (p<0.001) and more chemotherapy (p<0.001). In the multivariable Cox proportional hazard regression analysis, the PMRT group presented improved survival in terms of breast cancer specific survival (BCSS) (HR,0.739; 95% CI, 0.679–0.805; p<0.001) and overall survival (OS) (HR, 0.721; 95% CI, 0.670–0.777; p<0.001). After stratified according to positive axillary lymph nodes, the PMRT group showed improved BCSS and OS in the LN 1 to 3 subgroup (HR, 0.738, 95% CI, 0.639–0.853, p<0.001 and HR, 0.684, 95% CI, 0.604–0.776, p<0.001, respectively). For patients with 1-3 positive axillary lymph nodes and T1-2 tumors, the PMRT group still showed improved survival in terms of BCSS and OS (HR, 0.826, 95% CI, 0.688–0.992, p=0.04 and HR, 0.751, 95% CI, 0.643–0.878, p<0.001, respectively). In the subgroup analysis, PMRT remained a significant favorable prognostic factor in T2 and Her2-/HR+ subtype (p<0.05). This study suggests that even in the era of modern therapy, PMRT can confer a survival benefit to breast cancer patients with 1-3 positive axillary lymph nodes. Furthermore, for patients with 1-3 positive axillary lymph nodes and T1-2 tumors, PMRT can still provide survival benefits.

scholarly journals AB0292 EFFICACY AND SAFETY OF TWO BIOSIMILAR ETANERCEPT AFTER THE SWITCH FROM THEIR CORRESPONDING ORIGINATOR IN THE TREATMENT OF PATIENTS WITH AUTOIMMUNE ARTHRITIS; A RETROSPECTIVE ANALYSIS IN A REAL LIFE SETTING

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1445.1-1445
Author(s):  
F. Girelli ◽  
A. Ariani ◽  
M. Bruschi ◽  
A. Becciolini ◽  
L. Gardelli ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Disease Duration ◽  
Retention Rate ◽  
Real Life ◽  
Autoimmune Arthritis ◽  
Proportional Hazard ◽  
Hazard Regression ◽  
Cox Proportional Hazard ◽  
Real Life Setting ◽  
Cox Proportional Hazard Regression

Background:The available biosimilars of etanercept are as effective and well tolerated as their bio originator molecule in the naive treatment of chronic autoimmune arthritis. More data about the switching from the bio originator are needed.Objectives:To compare the clinical outcomes of the treatment with etanercept biosimilars (SB4 and GP2015) naïve and after the switch from their corresponding originator in patients affected by autoimmune arthritis in a real life settingMethods:We retrospectively analyzed the baseline characteristics and the retention rate in a cohort of patients who received at least a course of etanercept (originator or biosimilar) in our Rheumatology Units from January 2000 to January 2020. We stratified the study population according to biosimilar use. Descriptive data are presented by medians (interquartile range [IQR]) for continuous data or as numbers (percentages) for categorical data. Drug survival distribution curves were computed by the Kaplan-Meier method and compared by a stratified log-rank test. A Cox proportional hazards regression analysis stratified by indication, drug, age, disease duration, sex, treatment line, biosimilar use and prescription year was performed. P values≤0.05 were considered statistically significant.Results:477 patients (65% female, median age 56 [46-75] years, median disease duration 97 [40.25-178.75] months) treated with etanercept were included in the analysis. 257 (53.9%) were affect by rheumatoid arthritis, 139 (29.1%) by psoriatic arthritis, and 81 (17%) by axial spondylarthritis. 298 (62.5%) were treated with etanercept originator, 97 (20.3%) with SB4, and 82 (17.2%) with GP2015. Among the biosimilars 90/179 (50.3%) patients were naïve to etanercept treatment. Among the 89 switchers we observed 8 treatment discontinuations: one due to surgical infection complication, three due to disease flare, two due to subjective worsening and one due to remission. The overall 6- and 12-month retentions rate were 92.8% and 80.2%. The 6- and 12-month retention rate for etanercept, SB4 and GP2015 were 92.7%, 93.4% and 90.2%, and 82%, 74.5% and 88.1% respectively, without significant differences among the three groups (p=0.374). Patients switching from originator to biosimilars showed and overall higher treatment survival when compared to naive (12-month retention rate 81.2% vs 70.8%, p=0.036). The Cox proportional hazard regression analysis highlighted that the only predictor significantly associated with an overall higher risk of treatment discontinuation was the year of prescription (HR 1.08, 95% CI 1.04 to 1.13; p<0.0001).Conclusion:In our retrospective study etanercept originator and its biosimilars (SB4 and GP2015) showed the same effectiveness. Patients switching from originator to biosimilar showed an significant higher retention rate when compared to naive. The only predictor of treatment discontinuation highlighted by the Cox proportional hazard regression analysis was the year of treatment prescription.Disclosure of Interests:Francesco Girelli: None declared, Alarico Ariani: None declared, Marco Bruschi: None declared, Andrea Becciolini Speakers bureau: Sanofi-Genzyme, UCB and AbbVie, Lucia Gardelli: None declared, Maurizio Nizzoli: None declared

scholarly journals Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongwei Yu ◽  
Xianqi Meng ◽  
Huang Chen ◽  
Jian Liu ◽  
Wenwen Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Feature Selection ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Training Dataset ◽  
Validation Dataset ◽  
Gray Level ◽  
Breast Cancer Patients ◽  
Short Run

ObjectivesThis study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer.MethodsData from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann–Whitney U test to evaluate the level of TILs in the low and high groups.ResultsAmong the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738–0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615–0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively).ConclusionRadiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.

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).

Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

2021 ◽  
Vol 16 ◽  
Author(s):  
Dongqing Su ◽  
Qianzi Lu ◽  
Yi Pan ◽  
Yao Yu ◽  
Shiyuan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

scholarly journals Prognostic nomogram for patients with non-metastatic HER2 positive breast cancer in a prospective cohort

2019 ◽  
Vol 34 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Chuanxu Luo ◽  
Xiaorong Zhong ◽  
Zhu Wang ◽  
Yu Wang ◽  
Yanping Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Prospective Cohort ◽  
Proportional Hazard ◽  
Her2 Positive ◽  
Her2 Positive Breast Cancer ◽  
Cox Proportional Hazard ◽  
Cox Proportional Hazard Regression ◽  
Positive Breast Cancer

Purpose: A nomogram is a reliable tool to generate individualized risk prediction by combining prognostic factors. We aimed to construct a nomogram for predicting the survival in patients with non-metastatic human epidermal growth factor receptor 2 (HER2) positive breast cancer in a prospective cohort. Methods: We analyzed 1304 consecutive patients who were diagnosed with non-metastatic HER2 positive breast cancer between January 2008 and December 2016 in our institution. Independent prognostic factors were identified to build a nomogram using the COX proportional hazard regression model. The prediction of the nomogram was evaluated by concordance index (C-index), calibration and subgroup analysis. External validation was performed in a cohort of 6379 patients from the Surveillance, Epidemiology, and End Results (SEER) database. Results: Through the COX proportional hazard regression model, five independent prognostic factors were identified. The nomogram predicting overall survival achieved a C-index of 0.78 in the training cohort and 0.74 in the SEER cohort. The calibration plot displayed favorable accordance between the nomogram prediction and the actual observation for 3-year overall survival in both cohorts. The quartiles of the nomogram score classified patients into subgroups with distinct overall survival. Conclusion: We developed and validated a novel nomogram for predicting overall survival in patients with non-metastatic HER2 positive breast cancer, which presented a favorable discrimination ability. This model may assist clinical decision making and patient–clinician communication in clinical practice.

The prognostic importance of triple negative breast cancer: A population based study in India

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22219-e22219
Author(s):  
B. S. Ajaikumar ◽  
R. Rao ◽  
J. Prabhu ◽  
J. D. Kulkarni ◽  
P. K ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Triple Negative ◽  
Disease Free Survival ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Node Status ◽  
Disease Free

e22219 Background: Triple-negative (ER-negative, PR-negative, HER2/neu negative) breast cancer has distinct clinical and pathologic features, and is a clinical problem because of its typically high grade, relatively poor prognosis, aggressive behavior and lack of targeted therapies leaving chemotherapy as the mainstay of treatment. This study envisaged to analyse the influence of triple negativity status on survival and disease free survival in prospective cohort of breast cancer patients. Methods: Breast tumors of 215 women aged 30–75, diagnosed from 2004 were tested for ER, PR and HER2 positivity by immunohistochemistry and correlated with clinical outcomes such as recurrence, disease free survival and overall survival using Kaplan Meiers Survival analysis and Coxs regression analysis. The study cohort was followed up for 60 months or until death whichever was earlier. Results: Triple negativity significantly influenced disease free survival (46 ± 3, 41, 52) vs. non triple negative cohort (mean ± SE; 95%CI, 37 ± 2; 32, 40) and log rank = 2.1, p = 0.04. However triple negativity did not influence overall survival in months (56 ± 0; 55, 56) vs. non triple negative cohort (43 ± 1; 42, 45), (log rank = 1.78, p = 0.16). However, the mean disease free survival was (45 ± 7; 32, 58) months for patients >40 years age vs (37 ± 4; 33, 39) for patients < 40 years of age (log rank = 2.87, p =0.02). Stage of disease, node status, grade and menopausal status did not influence disease free survival significantly. However, Cox regression analysis did not predict significant effects of triple negativity on overall survival or disease free survival when controlled for confounding factors such as age, node status, stage etc Conclusions: Our observations suggest that triple negativity can significantly affect progression of breast cancer in Indian breast cancer patients and longer follow up is necessary (10 years) to determine its effects on survival. No significant financial relationships to disclose.

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