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.

scholarly journals Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yonghui Su ◽  
Yuchen Li ◽  
Rong Guo ◽  
Jingjing Zhao ◽  
Weiru Chi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Treatment Efficacy ◽  
Breast Imaging ◽  
Early Stage ◽  
Neoadjuvant Treatment ◽  
Extracellular Vesicle ◽  
Breast Cancer Patients ◽  
Mtorc1 Signaling ◽  
Efficacy Prediction

AbstractA large number RNAs are enriched and stable in extracellular vesicles (EVs), and they can reflect their tissue origins and are suitable as liquid biopsy markers for cancer diagnosis and treatment efficacy prediction. In this study, we used extracellular vesicle long RNA (exLR) sequencing to characterize the plasma-derived exLRs from 112 breast cancer patients, 19 benign patients and 41 healthy participants. The different exLRs profiling was found between the breast cancer and non-cancer groups. Thus, we constructed a breast cancer diagnostic signature which 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 with breast imaging could increase the diagnosis accuracy for breast cancer patients. Moreover, we enrolled 58 patients who received neoadjuvant treatment and identified an exLR (exMSMO1), which could distinguish pathological complete response (pCR) patients from non-pCR with an AUC of 0.790. Silencing MSMO1 could significantly enhance the sensitivity of MDA-MB-231 cells to paclitaxel and doxorubicin through modulating mTORC1 signaling pathway. This study demonstrated the value of exLR profiling to provide potential biomarkers for early detection and treatment efficacy prediction of breast cancer.

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 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 An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine

2021 ◽  
Vol 11 ◽  
Author(s):  
Mozhi Wang ◽  
Zhiyuan Pang ◽  
Yusong Wang ◽  
Mingke Cui ◽  
Litong Yao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Immune System ◽  
T Cell ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Cancer Patients ◽  
Immune Status ◽  
Support Vector ◽  
Breast Cancer Patients

Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4+/CD8+ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3+CD8+ cytotoxic T cell percent; CD16+CD56+ NK cell absolute value; and CD3+CD4+ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention.

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.

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