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.

scholarly journals Identification of the prognostic value of ferroptosis-related gene signature in breast cancer patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ding Wang ◽  
Guodong Wei ◽  
Ju Ma ◽  
Shuai Cheng ◽  
Longyuan Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Cancer Patients ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Kaplan Meier

Abstract Background Breast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women’s health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients’ survival. Methods Gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score. Results We constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001). Conclusion Our study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients’ prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

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.

scholarly journals Identifies Immune-Related Gene Pair Signature Associated with Breast Cancer Prognosis

2021 ◽  
Author(s):  
Tianwei Sun ◽  
Qixing Tan ◽  
Changyuan Wei
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Gene Pair ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Future Research ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Immune Related Gene ◽  
Immune Related Genes

Abstract Background: Breast cancer (BC) is the cancer with the largest number of deaths in women. There is growing evidence that immunity plays an important role in the prognosis of breast cancer. Methods: In this study, we developed and validated an immune-related gene pair signature (IRGPs) to predict the survival of breast cancer patients. Screening immune-related genes from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database for the construction of IRGPs, and patients with breast cancer in these two cohorts were assigned to low- and high- risk subgroups. Additionally, we used Kaplan-Meier survival analysis, univariate and multivariate Cox analysis to investigate IRGPs and their individualized prognostic characteristics, and analysis of immune cell infiltration in breast cancer. Results: A 47-IRGP signature was constructed from 2498 immune genes, which could significantly predict the overall survival (OS) of breast cancer patients in the TCGA and GEO cohorts. Immune infiltration analysis showed that a variety of immune cells are significantly related to the prognostic effects of IRGP characteristics in breast cancer patients, especially CD8+ T cells and macrophages. Conclusions: The IRGP signature constructed in this study can help determine the prognosis of breast cancer and provide new ideas and basis for future research on the role of immune-related genes in breast cancer patients.

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.

Disparities within luminal breast cancer: Clinical and molecular features of African American and non-Hispanic white patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1009-1009
Author(s):  
Kent Hoskins ◽  
Oana Cristina Danciu ◽  
Vijayakrishna K. Gadi ◽  
Yael Simons ◽  
Lisa Eileen Blumencranz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
African American ◽  
High Risk ◽  
Cancer Patients ◽  
Gene Signature ◽  
Tumor Type ◽  
Breast Cancer Patients ◽  
Luminal B ◽  
Hispanic White ◽  
White Patients

1009 Background: African American breast cancer patients (AA) are diagnosed younger, have more high-risk features, and poorer clinical outcomes than non-Hispanic White patients (NHW), despite similar treatments. Although comorbidities such as obesity and metabolic syndrome may contribute to differences, ancestry-specific factors and effects of structural violence that disproportionately afflict AA individuals may influence tumor biology and outcomes. We previously reported differentially expressed genes (DEGs) associated with tumor aggressiveness in Basal tumors from AA compared with NHW (Sharma et al., 2020). Here, we compare DEGs in luminal tumors between AA and NHW. Methods: The prospective, observational FLEX study (NCT03053193) includes stage I-III breast cancer patients who receive 70-gene signature (MammaPrint/MP)/80-gene signature (BluePrint/BP) testing and consent to full transcriptome and clinical data collection. AA (n=364) and NHW (n=400, random selection) with BP luminal tumors, enrolled from 2017 to present, were included. Race/ethnicity was self-reported. AA were younger than NHW (mean, 59 vs. 62 years, p=0.001); thus, an age-matched subset (n= 360 AA, NHW) was compared. Differential gene expression analysis was performed with R limma package. Comparisons were made between AA and age-matched or randomly selected NHW in: (1) all, (2) luminal A, (3) luminal B, and (4) luminal B, obese. DEGs with FDR<0.05 were significant. Different fold change (FC) thresholds were evaluated. Results: Compared with age-matched NHW, AA were similar in menopausal status, T stage, grade, and tumor type; obesity, T2DM status, and nodal stage were significantly different ( p<0.01). Tumors from AA were more often MP high risk ( p<0.001), regardless of age matching. Luminal B AA vs. age-matched NHW comparison resulted in more DEGs (n=1070) than other comparisons; however, most were FC<2. Notably, 5/6 DEGs ( PSPH, NOTCH2NL, POLR1A, MAP1LC3P and RPS26P10) in basal tumors (Nunes et al. 2019) were also identified here. Of 9 DEGs (FC>1.7) in the luminal B age-matched comparison, 2 ( PSPH and LINC01139) were also found in the luminal B, obese subset. Consistently upregulated DEGs in AA were associated with metabolism, translation, and cellular stress response pathways. Conclusions: We found significant transcriptomic differences between luminal tumors from AA and NHW, when controlling for age, obesity, and genomic classification. A subset of DEGs in luminal B tumors were consistent with those in Basal tumors, suggesting that similar race-associated factors drive DEGs regardless of tumor subtype. DEGs that may be unique to AA luminal tumors were also found. This study suggests that some biological differences in breast tumors may result from patient ancestry or shared adverse socioeconomic exposures and underscores the need for inclusion of diverse patient groups in clinical trials. Clinical trial information: NCT03053193.

scholarly journals Evaluation of malignancy-risk gene signature in breast cancer patients

2009 ◽  
Vol 120 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Dung-Tsa Chen ◽  
Aejaz Nasir ◽  
Chinnambally Venkataramu ◽  
William Fulp ◽  
Mike Gruidl ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Malignancy Risk ◽  
Risk Gene

scholarly journals Karakteristik Klinikopatologik Pasien Kanker Payudara dengan Metastasis Tulang di RSUP Sanglah pada Tahun 2014 - 2018

e-CliniC ◽  
2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Putu Krishna B. S. Putra ◽  
I Wayan J. Sumadi ◽  
Ni Putu Sriwidyani ◽  
IG Budhi Setiawan
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Invasive Carcinoma ◽  
Molecular Subtype ◽  
Metastatic Breast ◽  
Breast Cancer Patients ◽  
Luminal B ◽  
Histopathological Type

Abstract: Breast cancer is the most common cancer in woman. Metastasis often occurs especially to the bones. This study was aimed to determine the characteristics of breast cancer patients with bone metastasis. This was a descriptive study with a cross-sectional design. Samples were 46 breast cancer patients with bone metastasis recorded at Sanglah Hospital from 2014 until 2018. Data of pathological examination archives of Oncology Surgery Division Medical Faculty of Udayana University/Sanglah General Hospital were used to obtain the clinicopathological characteristics of metastatic breast cancer patients based on age, lateralization, histopathological type, and tumor molecular subtype. The results showed that most cases of metastatic breast cancer were aged 40-49 years as many 21 patients (45.7%), minimal difference in lateralization between right breast as many 22 patients (47.8%) and left breast 23 patients (50%). The most common histopathological type was invasive carcinoma of no special type as many 34 patients (73.9%). The most common tumor subtype was the luminal B subtype as many 21 patients (45.7%). In conclusion, most patients of breast cancer with bone metastasis were 40-49 years old, invasive carcinoma of no special type, molecular subtype of luminal B, and no significant difference between lateralization to the right and left breast.Keywords: breast cancer, bone, metastasis, clinicopathological caharacteristics Abstrak: Kanker payudara merupakan jenis kanker yang paling sering dijumpai pada wanita. Metastasis sering terjadi terutama pada tulang. Penelitian ini bertujuan untuk mengetahui karakteristik pasien kanker payudara dengan metastasis tulang di RSUP Sanglah Denpasar. Jenis penelitian ialah deskriptif dengan desain potong lintang. Sampel penelitian ialah 46 pasien kanker payudara dengan metastasis tulang yang tercatat di RSUP Sanglah tahun 2014-2018. Data diambil dari arsip hasil pemeriksaan patologi di Subdivisi Bedah Onkologi, Departemen/Kelompok Staf Medis (KSM) Bedah Fakultas Kedokteran Universitas Udayana (FK UNUD)/RSUP Sanglah untuk mendapatkan karakteristik klinikopatologi pasien kanker payudara metastasis tulang berdasarkan usia, lateralisasi, tipe histopatologik, dan subtipe molekuler tumor. Hasil penelitian menunjukkan kasus terbanyak terjadi pada rentang usia 40-49 tahun sebanyak 21 orang (45,7%), dengan lateralisasi tidak jauh berbeda antara payudara kanan sebanyak 22 orang (47,8) dan kiri sebanyak 23 orang (50%). Tipe histopatologik yang lebih sering ditemukan yaitu invasive carcinoma of no special type sebanyak 34 orang (73,9%). Subtipe molekuler yang paling banyak ditemukan ialah subtipe luminal B sebanyak 21 orang (45,7%). Simpulan penelitian ini pasien kanker payudara dengan metastasis tulang berada pada rentang usia 40-49 tahun, invasive carcinoma of no special type, subtipe molekuler luminal B. dan lateralisasi payudara kanan dan kiri tidak jauh berbeda.Kata kunci: kanker payudara, metastasis, tulang, karakteristik klinikopatologik

Relationship between tumor size and metastatic site in patients with stage IV breast cancer: A large SEER-based study.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13065-e13065
Author(s):  
Qian Dong ◽  
Mi Zhang ◽  
Da Jiang
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Tumor Size ◽  
Stage Iv ◽  
Breast Cancer Patients ◽  
Luminal A ◽  
Chi Square ◽  
Metastatic Site ◽  
Luminal B ◽  
Stage Iv Breast Cancer

e13065 Background: To analyze the correlation between tumor size and metastatic site in first-diagnosed stage IV breast cancer patients. Methods: Stage IV breast cancer patients diagnosed from 2010 to 2015 were screened by the Surveillance, Epidemiology, and End Results (SEER) database. The characteristics of clinical variables were represented by a frequency table, and the Chi-square test was used for comparison. At the same time, the Chi-square test was used to analyze the relationship between tumor size and organ metastasis. Correlation between tumor size and the prognosis of patients was contributed by KM curve and Log-rank test. Results: Regardless of tumor size, the proportion of bone metastasis was higher and brain metastasis was lower in breast cancer patients. There were significant differences in the site of metastases based on different subtype. Luminal A and Luminal B breast cancer had the highest proportion of bone metastases; brain metastasis accounted for the highest proportion in triple-negative breast cancer (TNBC); while the incidence of liver metastasis was the highest in Her-2(+) breast cancer. At the same time, the results indicated that Luminal A breast cancer with a tumor size > 5 cm was more likely to develop multi-site metastasis and lung metastasis, while Luminal B breast cancer with a tumor size ≤ 5 cm was more likely to develop liver metastasis. The results also revealed that TNBC patients with a tumor size of 0 - 2cm were more likely to develop bone metastasis than those with a tumor size > 5 cm, and the incidence of lung metastasis in triple-negative patients showed an increasing trend with the increase of tumor size. Conclusions: Based on subtype, we found that there was a significant difference between tumor size and metastatic site in patients with stage IV breast cancer, and the difference was statistically significant. This study provided evidence-based basis for decision-making of stage IV breast cancer treatment.

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